Variations of nodule invasiveness and morphology relate to prognosis and patient outcomes. One approach for diagnosing disease is histopathological examination of biopsy tissue. The examination may produce a diagnostic profile based on attributes including cell morphology, cytoplasmic changes, cell density, or cell distribution. Visual characterization of tumor morphology is, however, time consuming, and expensive. Visual characterization is also subjective and thus suffers from inter-rater and intra-rater variability. Conventional visual characterization of nodule morphology by a human pathologist may therefore be less than optimal in clinical situations where timely and accurate classification can affect patient outcomes.
Computed tomography (CT) is frequently used to image nodules. For example, chest CT imagery may be used to detect and diagnose non-small cell lung cancer. However, conventional approaches to analyzing chest CT imagery have been challenged when attempting to distinguish a benign granuloma (Gr) from malignant adenocarcinoma (AC). For example, conventional CT-based approaches may find it difficult, if even possible at all, to reliably discriminate nodules caused by benign fungal infections from non-small cell lung cancer nodules. Histoplasmosis is a common endemic fungal infection in the United States. Granulomas secondary to histoplasmosis infection may appear identical to malignant lung nodules in CT imagery.
Other cancer types pose challenges when determining treatments or predicting response to treatment. Magnetic resonance imaging (MRI) is a common medical imaging modality for preparing or analyzing neo-adjuvant chemotherapy (NAC) for breast cancer. Administered prior to surgery, NAC can reduce the extent of tumor burden and increase a patient's surgical options. The ideal outcome of NAC is pathological complete response (pCR), which is the complete disappearance of residual invasive tumor cells within excised breast tissue. However, less than 25% of breast cancer patients who undergo NAC will achieve pCR.
Since radiologists may be challenged to reliably distinguish Gr secondary to benign fungal infections from AC in situ using conventional CT approaches in clinically optimal or relevant time frames, invasive procedures may be performed that ultimately result in a negative diagnosis. For example, many patients with benign granulomas are subjected to unnecessary surgical resections and biopsies. These invasive procedures take time, cost money, and put a patient at additional risk. As the number of routine chest CT scans increases with the wide-spread adoption of CT-based lung cancer screening protocols, it would be beneficial to reduce unnecessary thoracotomies, bronchoscopies, biopsies, and other invasive procedures. Similarly, breast cancer patients would benefit from an accurate, non-invasive predictor of pCR that facilitated more accurate and effective targeting of NAC.